g2_calc_recency.py

#create a generic user dataframe to keep CustomerID and new segmentation scores
tx_user = pd.DataFrame(tx_data['CustomerID'].unique())
tx_user.columns = ['CustomerID']

#get the max purchase date for each customer and create a dataframe with it
tx_max_purchase = tx_uk.groupby('CustomerID').InvoiceDate.max().reset_index()
tx_max_purchase.columns = ['CustomerID','MaxPurchaseDate']

#we take our observation point as the max invoice date in our dataset
tx_max_purchase['Recency'] = (tx_max_purchase['MaxPurchaseDate'].max() - tx_max_purchase['MaxPurchaseDate']).dt.days

#merge this dataframe to our new user dataframe
tx_user = pd.merge(tx_user, tx_max_purchase[['CustomerID','Recency']], on='CustomerID')

tx_user.head()

#plot a recency histogram

plot_data = [
    go.Histogram(
        x=tx_user['Recency']
    )
]

plot_layout = go.Layout(
        title='Recency'
    )
fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.iplot(fig)